{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab we'll look at images as data. In particular, we'll show how to use standard Python libraries to visualize and distort images, switch between matrix and vector representations, and run machine learning to perform a classification task. We'll be using the ever popular MNIST digit recognition data set. This will be a two part lab, comprised of the following steps:\n",
    "\n",
    "Part1:\n",
    "1. Generate random matrices and visualize as images\n",
    "2. Data Prep and Exploration\n",
    "-- Load MNIST data\n",
    "-- Convert the vectors to a matrix and visualize\n",
    "-- Look at pixel distributions\n",
    "-- Heuristic feature reduction\n",
    "-- Split data into train and validation\n",
    "\n",
    "Part 2:\n",
    "3. Classification\n",
    "-- One vs. All using logisitic regression.\n",
    "-- Random Forests on all\n",
    "4. Error analysis\n",
    "-- Visualize confusion matrix\n",
    "-- Look at specific errors\n",
    "5. Generate synthetic data and rerun RF\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#Student: change the data directory to where you have the data\n",
    "data_dir = '/Users/briand/Desktop/ds course/ipython/data/'\n",
    "data_train = data_dir + 'mnist_train_10k.csv'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Random Matrices and View as Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before looking at actual data, let's introduce the basic image data construct in Python. A color image is generally a 3-dimensional array with dimensions: NxMx3. The last dimension is the color channel, and the first two represent the X and Y grid of the image. The values are typically integer-valued pixel intensities for each color channel, with values between 0 and 255.\n",
    "\n",
    "The first thing we'll do is generate a random image and look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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3yqvbOe5uwBKXnVyeXMjDTYl0y01l2NRH+K+YyCzXRfQcWsCBsA+cThnDQU0fdj2Zw+ba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v3k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WlNHalBbiSY0nTyYZ4kwXp65LOvRtQJwOKM7SjNAF4sv6TYsfFNHftHdk3chOSdlcNHJv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\nqjcehFD4Y0idSEL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RGF91g+c2dtjKiuLrGhc0nC/F2k2B8FE4AS6jbuj5meOvaTbWT4iD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\nBpEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Student: generate a 3-dimensional random array of integers in the range of [0,255]\n",
    "x = 256\n",
    "y = 256\n",
    "d = 3\n",
    "\n",
    "rand_img = np.random.randint(0, 256, size=(x, y, d), dtype='int')\n",
    "img = plt.imshow(rand_img, interpolation='nearest')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now do it again with a much smaller grid to see how it looks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#another way\n",
    "x = 32\n",
    "y = 32\n",
    "d = 3\n",
    "\n",
    "rand_img = np.random.randint(0, 256, size=(x, y, d), dtype='int')\n",
    "img = plt.imshow(rand_img, interpolation='nearest')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Prep and Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
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       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
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       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0       0    ...            0         0         0         0         0   \n",
       "1       0    ...            0         0         0         0         0   \n",
       "2       0    ...            0         0         0         0         0   \n",
       "3       0    ...            0         0         0         0         0   \n",
       "4       0    ...            0         0         0         0         0   \n",
       "\n",
       "   pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0         0  \n",
       "1         0         0         0         0         0  \n",
       "2         0         0         0         0         0  \n",
       "3         0         0         0         0         0  \n",
       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the data and take a look at the first few rows\n",
    "train = pd.read_csv(data_train)\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 785)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check to see the size of the data (rows and columns). Remember the first column is the label\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do a little prep on the data. We need to split the training data to produce a validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "val_pct = 0.1\n",
    "X = train.drop('label', 1)\n",
    "y = train.label\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=val_pct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this data is in a single vector format (i.e., the dimensionality of each record is 1xK, and not in the typical 2-dim photo layout). Before doing any data mining, let's first explore the data visually.\n",
    "\n",
    "The first thing we can do is look at the actual images. What better way to visualize image data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hzNXCCy8cso6c0pc76a9dw1nMnZ1XAACKoXkFAKAYmlcAAIqheQUAoBgmIebh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\nQtaXBlxoj2WWWSZko0aN6vb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AAMXQvAIAUAzNKwAAxegXA1urrLJKu0voFXIfOn/11VdD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\nAgBQjP8PkItxrXwzeAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#First let's build a function that converts a vector into an image matrix and plots it\n",
    "\n",
    "def plot_vector(vec):\n",
    "    '''\n",
    "    Takes in image vector, transforms and plots\n",
    "    '''\n",
    "    v_sq = np.reshape(vec, (28,28))\n",
    "    plt.imshow(v_sq, interpolation='nearest', cmap = 'gray')\n",
    "\n",
    "#plot the first few number images along with their label.\n",
    "s = 16\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "for i in range(16):\n",
    "    fig.add_subplot(4, 4, i + 1)\n",
    "    plot_vector(X_train.iloc[i].values)\n",
    "    plt.title(str(y_train.iloc[i]))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the black and white images, many pixels might only have black (x=0) values. These won't be useful for modeling, so we can drop them. Write a function that finds all of the features with stdev=0 and then drop them from the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((9000, 784), (9000, 675))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The describe method returns a dataframe with features as columns and distributional stats as rows\n",
    "#hint: use the dataframe.describe() and drop() methods and this can be done in 2 lines\n",
    "def get_no_variance_features(df):\n",
    "    '''\n",
    "    Input: a data frame\n",
    "    Ouput: a list of features with no variance\n",
    "    '''\n",
    "    desc_df = df.describe()\n",
    "    return desc_df.loc['std'][(desc_df.loc['std']==0)].index.values\n",
    "\n",
    "drop_list = get_no_variance_features(X_train)\n",
    "\n",
    "X_train_filt = X_train.drop(drop_list, 1)\n",
    "X_val_filt = X_val.drop(drop_list, 1)\n",
    "\n",
    "X_train.shape, X_train_filt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now take a look at the distribution of pixel values in a more traditional way. Let's also look at the distribution by class to see if we see any differences. As there are many pixels, let's look at the top 4 by pixel variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Get the standard deviation of each feature (using describe() as in above)\n",
    "#Sort the results and pull out the top 4 feature names\n",
    "df_std = X_train.describe().loc['std']\n",
    "df_std.sort_values(ascending=False, inplace=True)\n",
    "top4_list = df_std.index.values[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q+3abs9vrc91bO3+mzdmN37+tKIzL3K50xIqIiRExqXsYOB74JTvegvUs4N9b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\nAkX4+vtuxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now for each feature plot the histogram by each class label\n",
    "Y_labels = range(10)\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "for i, var in enumerate(top4_list):\n",
    "    ax = fig.add_subplot(2,2,i+1)\n",
    "    ax.set_xlim([0,256])\n",
    "    ax.set_ylim([0,0.5])\n",
    "    for y in Y_labels:\n",
    "        plt.hist(X_train[var][(y_train==y)], bins=100, density=True, histtype='step')\n",
    "        plt.title(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given how polarized the pixels are, this is a bit hard to read. Next let's just look at means by label group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel378</th>\n",
       "      <th>pixel406</th>\n",
       "      <th>pixel461</th>\n",
       "      <th>pixel437</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.823129</td>\n",
       "      <td>4.247166</td>\n",
       "      <td>3.588435</td>\n",
       "      <td>28.976190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241.553299</td>\n",
       "      <td>245.649746</td>\n",
       "      <td>218.771574</td>\n",
       "      <td>4.810152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.580576</td>\n",
       "      <td>122.861259</td>\n",
       "      <td>179.108858</td>\n",
       "      <td>137.573106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>197.195390</td>\n",
       "      <td>152.374314</td>\n",
       "      <td>43.328211</td>\n",
       "      <td>132.499451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.989583</td>\n",
       "      <td>116.673611</td>\n",
       "      <td>170.149306</td>\n",
       "      <td>213.247685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>126.016149</td>\n",
       "      <td>120.980124</td>\n",
       "      <td>58.673292</td>\n",
       "      <td>102.585093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79.879085</td>\n",
       "      <td>124.840959</td>\n",
       "      <td>134.355120</td>\n",
       "      <td>133.626362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.145474</td>\n",
       "      <td>34.321121</td>\n",
       "      <td>47.153017</td>\n",
       "      <td>184.339440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>179.722738</td>\n",
       "      <td>214.118329</td>\n",
       "      <td>189.256381</td>\n",
       "      <td>97.008121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101.971366</td>\n",
       "      <td>143.935022</td>\n",
       "      <td>119.210352</td>\n",
       "      <td>200.349119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pixel378    pixel406    pixel461    pixel437\n",
       "y                                                \n",
       "0    9.823129    4.247166    3.588435   28.976190\n",
       "1  241.553299  245.649746  218.771574    4.810152\n",
       "2   81.580576  122.861259  179.108858  137.573106\n",
       "3  197.195390  152.374314   43.328211  132.499451\n",
       "4   53.989583  116.673611  170.149306  213.247685\n",
       "5  126.016149  120.980124   58.673292  102.585093\n",
       "6   79.879085  124.840959  134.355120  133.626362\n",
       "7   20.145474   34.321121   47.153017  184.339440\n",
       "8  179.722738  214.118329  189.256381   97.008121\n",
       "9  101.971366  143.935022  119.210352  200.349119"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_samp = pd.DataFrame(X_train[top4_list])\n",
    "X_samp['y'] = y_train\n",
    "X_samp.groupby('y').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we start to see how the distributions differ by class?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our data generally explored and prepped and a little intuition about how pixel values might help us discrimate between the digits, let's start to use our predictive modeling tools.\n",
    "\n",
    "We'll first start with a simple linear model. Also, this is a multi-class scenario so we can't just use and out-of-the-box logistic regression. Luckliy, like for most things machine learning, SkLearn has a tool for that.\n",
    "\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "          n_jobs=1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#Next let's build the model\n",
    "ova1 = OneVsRestClassifier(LogisticRegression())\n",
    "ova1.fit(X_train_filt, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look under the hood of the OneVsRestClassifier object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dict_keys(['n_jobs', 'label_binarizer_', 'estimator', 'estimators_', 'classes_']),\n",
       " 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ova1.__dict__.keys(), len(ova1.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False),\n",
       " LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False),\n",
       " LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "           penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "           verbose=0, warm_start=False)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ova1.estimators_[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now let's evaluate on test data using a confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#First execute this function\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    acc = np.diag(cm).sum() / float(cm.sum())\n",
    "    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title('{},Acc={}'.format(title, acc))\n",
    "    #plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, np.round(cm[i, j],3),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CyNh7wKfNnr0HGHzbG1x05ZOMeykVgAO//+Fd3+/SVsz5dD25uXkcLy/PYtrC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\nAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#Next, predict the class on validation data and generate the confusion_matrix\n",
    "preds_ova = ova1.predict(X_val_filt)\n",
    "cm = confusion_matrix(y_val, preds_ova, labels=range(10))\n",
    "\n",
    "#Now use the function above to plot it\n",
    "np.set_printoptions(precision=3)\n",
    "plt.figure(figsize = (10,10))\n",
    "plot_confusion_matrix(cm, classes=range(10), normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the above, are any of the errors a bit weird?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wE2BH8fDlVPb7vw2MBtZSOdW3tc68fKrPrMMaPdXn7/Ob7WH8fX4zS3L4zTLl8JtlyuE3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\ni20AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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apFWS7pU0qsp7eiUtl7S8qUrNrKXq/g0/SSOAfwNui4hHJfUAbwEB3EJ2avCHNZbhw36z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\nh98sUQ6/WaL+F4uxJtHBq1OCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1Aw88MDntkUcemaxv3bo1WV+6dGnNWupr0ACbNm1K1ruZb9FtZkkOv1mmHH6zTDn8Zply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\nlMNvlimH3yxTDr9Zphx+s0z9L4jmX3y51wqwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Uw2+WKYffLFP+6m6zYcZf3W1mSQ6/WaYcfrNMOfxmmXL4zTLl8JtlyuE3y5TDb5Yph98s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\nvscYvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cpGclfbWYfqWkTZKeKG4zO9+umbWLIiI9gzQeGB8RayWNAR4HTgPOBN6OiO82/GRS+snM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\nGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g9vklHQOcBCzPJ10habWkBZIOrzJPt6QeST0NVWpmTTXga/sljQIeB2ZHxN2SxgCvAwH8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\nEuXwmyXK4TdLlMNvliiH3yxRDr9Zov4f/AvZhNdSOo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+VdIrktZI+vNs+DWSNkh6KXud1vpyzawodR/mIWkMMCYiXpQ0AngBOAuYDrwbEdf3eWF+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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XP378+Kbq7bRnz55kfdeuXTVre/fubXU7NgDe85tlyuE3y5TDb5Yph98sUw6/WaYcfrNM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\n+H/Z6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GR8T4VjRsZq1RN/yqbOJvAJ6MiGuqSncCM4rnM4Dvtb49M2uX/uz2nwJMB34qaUUx7QvA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\nMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tN8R2mfbaa6+W5n/jjTfa1MnQEhG1Ls1vx1t+s0w5/GaZcvjNMuXwm2XK4TfLlMNvlimH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\nmXL4zTL1/7ySDIl8ltA8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0/I1c7RdwK7C+OvjFhcC9PgOsHWqTZlaeRq72nwH8EHgO2FNMvhqYDUyncti/AZhbXBxM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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize errors where (pred, true) in [(2,6), (8,2),(8,1)]\n",
    "\n",
    "pred_true_list = [(2,6), (8,2),(8,1)]\n",
    "'''\n",
    "Loop through the above list of tuples.\n",
    "Filter out the validation records where the prediction is the first element, and the truth is the second\n",
    "Then call the plot_vector function to visualize\n",
    "Add a title showing prediction and truth\n",
    "'''\n",
    "for pt in pred_true_list:\n",
    "    Xs = X_val[(y_val==pt[1]) & (preds_ova==pt[0])]\n",
    "    for i in range(Xs.shape[0]):\n",
    "        plt.figure()\n",
    "        plot_vector(Xs.iloc[i].values)\n",
    "        plt.title('pred={},truth={}'.format(pt[0],pt[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This accuracy is pretty good for an untuned LR on a sample of data. Can we do better with a random forest? Without running first, why might a RF do better in this case?\n",
    "\n",
    "# This should conclude Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/briand/anaconda/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 200)\n",
    "rf.fit(X_train_filt, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Iw3Ttg7MiO6HfKS29s7YWe9xnTp9WxuPeq+hxf3n2THU/1Xvce2b01szp05Sf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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Get the predictions and a confusion matrix, and plot like was done above\n",
    "preds_rf = rf.predict(X_val_filt)\n",
    "cm_rf = confusion_matrix(y_val, preds_rf, labels=range(10))\n",
    "\n",
    "np.set_printoptions(precision=3)\n",
    "plt.figure(figsize = (10,10))\n",
    "plot_confusion_matrix(cm_rf, classes=range(10), normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "One interesting aspect of image is data is that we can often improve our models by using synthetic data. The key is to produce such data in a way that it doesn't deviate too far from realistic examples. We can do this by making small pertubations to the image matrix, such as adding random noise, adding blurs and by performing small rotations.\n",
    "\n",
    "We'll do that here using tools from scipy alone (note there are other image libraries that offer even more functionality). We'll then take the augmented data set and build another random forest and see if we can improve our model.\n",
    "\n",
    "First let's create a function that adds random gaussian noise, rotates the angle of the number, and applies some gaussian smoothing to create blur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.ndimage import gaussian_filter \n",
    "#from scipy.misc import imrotate - note this has been deprecated\n",
    "\n",
    "def distort_image(vec, noise_std, rotation_angle, blur_std):\n",
    "    '''\n",
    "    Pass in a row of training data and distortion params\n",
    "    Return a distorted image\n",
    "    Params:\n",
    "    - vec: a row of MNIST data\n",
    "    - noise_std: parameter to control the standard deviation of random noise\n",
    "    - rotation_angle: degrees we will rotate the number\n",
    "    - blur_std: parameter to control the amount of local smoothing/blurring\n",
    "    \n",
    "    - Note: it is best to do the rotation first\n",
    "    '''\n",
    "    shape = (28,28)\n",
    "    mx = 255\n",
    "    mn = 0\n",
    "    img = np.reshape(vec, shape)\n",
    "    #commenting out - imrotate has been deprecated, use skimage.transform.rotate instead\n",
    "    #img_rot = imrotate(img, rotation_angle) \n",
    "    img_rot = img\n",
    "    noise = np.random.normal(0, noise_std, size = shape)\n",
    "    img_rot_blur = gaussian_filter(img_rot, blur_std) + noise\n",
    "    #Adding noise can create values out of allowable range, so have to clip\n",
    "    img_rot_blur[img_rot_blur>mx] = mx\n",
    "    img_rot_blur[img_rot_blur<mn] = mn\n",
    "    return img_rot_blur"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before building more training data, let's first plot some examples here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8Kl+bAlAYzCxQOZhXIP9CjDHdwhCqJK2TdI2k7ZKekjQnxrjKWWPe2KBBg8zb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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vec = X_train.iloc[10].values\n",
    "\n",
    "plt.figure(figsize = (12,12))\n",
    "angles = [0, -20, 20]\n",
    "for i,noise in enumerate([0.1, 30, 60]):\n",
    "    for j,blur in enumerate([0, 1, 2]):\n",
    "        img_rot_blur = distort_image(vec, noise, angles[i], blur)\n",
    "        plt.subplot(3,3,i*3+j+1)\n",
    "        plt.imshow(img_rot_blur, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we need to choose distortation parameters that are significant enough to create a new image, but don't make it unreadable. This is a subjective call (which can be tested empirically...how?). What are some reasonable values?\n",
    "\n",
    "Our next step will be to generate another 10k training data points, and then run a second random forest.\n",
    "\n",
    "Note to students: choose your own values here, including total new and the distortation parameters. We'll execute and then see as a class what works best (in a sense we'll parallelize the exploration of the parameter space)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = 10000\n",
    "\n",
    "new_xs = []\n",
    "new_ys = []\n",
    "for i in range(n):\n",
    "    #Get a random example, note this samples with replacement\n",
    "    samp_i = np.random.randint(0,X_train.shape[0])\n",
    "    vec = X_train.iloc[samp_i].values\n",
    "    #Now generate some parameters, choosing them randomly\n",
    "    angle = np.random.uniform(-20, 20)\n",
    "    noise = 30\n",
    "    blur = np.random.uniform(0,1)\n",
    "    #Get a random image and convert back to \n",
    "    new_xs.append(distort_image(vec, noise, angle, blur).ravel())\n",
    "    new_ys.append(y_train.iloc[samp_i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19000, 675), (19000,))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now convert this fake data to DataFrames and append to original\n",
    "X_train_fake = pd.DataFrame(new_xs, columns = X_train.columns)\n",
    "y_train_fake = pd.Series(new_ys)\n",
    "\n",
    "#Let's not forget to throw out the features we tossed out earlier\n",
    "X_train_fake = X_train_fake.drop(drop_list, 1)\n",
    "\n",
    "#Now let's concatenate the fake data to the original\n",
    "X_train_aug = pd.concat([X_train_filt, X_train_fake])\n",
    "y_train_aug = pd.concat([y_train, y_train_fake])\n",
    "\n",
    "X_train_aug.shape, y_train_aug.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UrHfK5OcVExOjdRmbdd31ozXu9rGSpIyMjZo1Y7rSP16v19MWacz11yoQCGjDhvWa/MJz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\nIuI0BWIRERERcZoCsYiIiIg47f8BZudEMTc0haoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rerun RF, make predictions and plot confusion matrix as is done above\n",
    "rf = RandomForestClassifier(n_estimators = 200)\n",
    "rf.fit(X_train_aug, y_train_aug)\n",
    "\n",
    "preds_rf = rf.predict(X_val_filt)\n",
    "\n",
    "cm_rf = confusion_matrix(y_val, preds_rf, labels=range(10))\n",
    "\n",
    "np.set_printoptions(precision=3)\n",
    "plt.figure(figsize = (10,10))\n",
    "plot_confusion_matrix(cm_rf, classes=range(10), normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
